Research article Special Issues

A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images


  • Received: 06 October 2023 Revised: 07 November 2023 Accepted: 19 November 2023 Published: 04 December 2023
  • The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.

    Citation: Xiaoli Li. A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21432-21450. doi: 10.3934/mbe.2023948

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  • The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.



    The Navier-Stokes equations are a typical nonlinear system, which model the mechanics law for fluid flow and have been applied in many fields. There are many research findings on the Navier-Stokes system, involving well-posedness, long-time behavior, etc., [1,2,3,4,5,6,7,8,9,10]. Furthermore, to simulate the fluid movement modeled by the Navier-Stokes equations, some regularized systems are proposed, such as the Navier-Stokes-Voigt equations. The Navier-Stokes-Voigt equations were introduced by Oskolkov in 1973, which describe the motion of Kelvin-Voigt viscoelastic incompressible fluid. Based on the global well-posedness of 3D Navier-Stokes-Voigt equations in [11], many interesting results on long-time behavior of solutions have been obtained, such as the existence of global attractor and pullback attractors, determining modes and estimate on fractal dimension of attractor [12,13,14] and references therein for details.

    The influence of past history term on dynamical system is well known, we refer to [15,16,17,18] for interesting conclusions, such as the global well-posedness, the existence of attractors and so on. In 2013, Gal and Tachim-Medjo [17] studied the Navier-Stokes-Voigt system with instantaneous viscous term and memory-type viscous term, and obtained the well-posedness of solution and exponential attractors of finite dimension. In 2018, Plinio et al. [18] considered the Navier-Stokes-Voigt system in [18], in which the instantaneous viscous term was completely replaced by the memory-type viscous term and the Ekman damping βu was presented. The authors showed the existence of regular global and exponential attractors with finite dimension. The presence of Ekman damping was to eliminate the difficulties brought by the memory term in deriving the dissipation of system.

    Some convergence results of solutions or attractors as perturbation vanishes for the non-autonomous dynamical systems without memory can be seen in [19,20,21,22]. However, there are few convergence results on the system with memory. Therefore, our purpose is to study the tempered pullback dynamics and robustness of the following 3D incompressible Navier-Stokes-Voigt equations on the bounded domain Ω with memory and the Ekman damping:

    {t(uαΔu)0g(s)Δu(ts)ds+(u)u+βu+p=fε(t,x), (t,x)Ωτ,divu=0, (t,x)Ωτ,u(t,x)=0, (t,x)Ωτ,u(τ,x)=u(τ), xΩ,u(τs,x)=φ(s,x), (s,x)Ω0, (1.1)

    where Ωτ=(τ,+)×Ω, Ωτ=(τ,+)×Ω, Ω0=(0,)×Ω, τR+ is the initial time, α>0 is a length scale parameter characterizing the elasticity of fluid, β>0 is the Ekman dissipation constant, u=(u1(t,x),u2(t,x),u3(t,x)) is the unknown velocity field of fluid, and p is the unknown pressure. The non-autonomous external force is fε(t,x)=f1(x)+εf2(t,x) (0ε<ε0), where ε0 is a fixed constant small enough. In addition, u(τ) is the initial velocity, and φ(s,x) denotes the past history of velocity. The memory kernel g: [0,)[0,) is supposed to be convex, smooth on (0,) and satisfies that

    g()=0, 0g(s)ds=1.

    In general, we give the past history variable

    η=ηt(s)=s0u(tσ)dσ, s0,

    which satisfies

    tη=sη+u(t).

    Also, η has the explicit representation

    {ηt(s)=s0u(tσ)dσ, 0<st,ηt(s)=η0(st)+t0u(tσ)dσ, s>t, (1.2)

    and

    ητ(s)=s0φ(σ)dσ.

    Next, we give the main features of this paper as follows.

    1) Inspired by [18,23], we provide a detailed representation and Gronwall type estimates for the energy of (1.1) dependent on ε in Lemma 2.1, with a focus on the parameters ω, Λ and the increasing function J(). Using these parameters, we construct the universe D and derive the existence of Dpullback absorbing sets, see Lemma 4.9.

    2) Via the decomposition method, we show that the process of the system has the property of Dκpullback contraction in the space NV, and the Dpullback asymptotic compactness is obtained naturally. Based on the theory of attractor in [1,24], the D-pullback attractors for the process {Sε(t,τ)} in NV are derived, see Theorem 3.3.

    3) When the perturbation parameter ε0 with the non-autonomous external force, the robustness is obtained via the upper semi-continuity of pullback attractors of (1.1) by using the technique in [18,21,22], see Theorems 3.2 in Section 3.

    This paper is organized as follows. Some preliminaries are given in Section 2, and the main results are stated in Section 3, which contains the global well-posedness of solution, the existence of pullback attractors and robustness. Finally, the detailed proofs are provided in Sections 4 and 5.

    ● The Sobolev spaces

    Let E={u|u(C0(Ω))3,divu=0}, H is the closure of E in (L2(Ω))3 topology with the norm and inner product as

    |u|=uH=(u,v)1/2, (u,v)=3j=1Ωuj(x)vj(x)dx,  u,vH.

    V is the closure of E in (H1(Ω))3 topology with the norm and inner product as

    u=uV=((u,u))1/2V, ((u,v))V=3i,j=1Ωujxivjxidx,  u,vV.

    Also, we denote

    ((u,v))Vα=(u,v)+α((u,v))V, ||u||2Vα=|u|2+α||u||2.

    H and V are Hilbert spaces with their dual spaces H and V respectively, and , denote the norm in V and the dual product between V and V respectively, and also H to itself.

    ● The fractional power functional spaces

    Let PL be the Helmholz-Leray orthogonal projection in (L2(Ω))3 onto H [3,7], and

    PL: HHH,

    where

    H={u(L2(Ω))3;  χ(L2loc(Ω))3: u=χ}.

    A=PLΔ is the Stokes operator with eigenvalues {λj}j=1 and orthonormal eigenfunctions {ωj}j=1.

    Define the fractional operator As by

    Asu=jλsj(u,ωj)ωj,  sR,  jZ+

    for u=j(u,ωj)ωj with the domain D(As)={u|AsuH}, and we use the norm of D(As) as

    u22s=|Asu|2=jλ2sj|(u,ωj)|2.

    Especially, denote W=D(A), and V=D(A1/2) with norm u1=|A1/2u|=u for any uV.

    ● The memory spaces

    For any s(0,), we define μ(s)=g(s), which is nonnegative, absolutely continuous, decreasing (μ0 almost everywhere) and

    κ=0μ(s)ds>0. (2.1)

    Also, there exists δ>0 such that

    μ(s)+δμ(s)0, a.e. s(0,). (2.2)

    Let

    MX=L2μ(R+;X), X=Vor W,

    which is a Hilbert space on R+ with inner product and norm

    ((η,ζ))MX=0μ(s)((η(s),ζ(s))Xds, ηMX=(0μ(s)||η(s)||2Xds)1/2.

    Moreover, the extended memory space can be defined as

    NX=X×MX

    equipped with the norm

    (u,η)2NX=u2X+η2MX.

    ● The bilinear and trilinear operators

    The bilinear and trilinear operators are defined as follows [8]

    B(u,v):=PL((u)v),   u,vV, (2.3)
    b(u,v,w)=<B(u,v),w>=3i,j=1Ωuivjxiwjdx. (2.4)

    Denote B(u)=B(u,u), B(u,v) is a continuous operator from V×V to V, and there hold

    b(u,v,v)=0, b(u,v,w)=b(u,w,v),  u,v,wV. (2.5)

    ● Some useful lemmas

    Lemma 2.1. ([23]) Assume that

    1) A nonnegative function h is locally summable on R+, and for any ε(0,ε0] and any tτ0 there holds

    εtτeε(ts)h(s)ds85supt0t+1th(s)ds<.

    2) The nonnegative function yε(t) is absolutely continuous on [τ,), and satisfies for some constants R,C00 that

    yε(t)Reε(tτ)+εptτeε(ts)h(s)yε(s)qds+C0ε1+r,

    where p,q,r0, and p1>(q1)(1+r)0.

    3) Let z(t)0 be a continuous function on (0,) equivalent to yε(t), which means there exist some constants M1, L0 such that

    z(t)Myε(t)M(z(t)+L).

    Then, there exist ω, Λ>0 and an increasing function J(): R+R+ such that

    z(t)J(MR)eω(tτ)+Λ(MC0+L).

    Remark 2.1. Under the assumptions in Lemma 2.1, there exists a constant θ(0,1) satisfying

    p1=pθθ+1q>0, p2=1θrθ>0.

    Denote

    p3=max{ε1/θ0,(2supt0t+1th(s)ds)1/p1,C1/p20}, p4=2max{6Rp13,1},

    then

    ω=ωθ,p,q,r,C0=12pθ3, Λ=Λθ,p,q,r,C0=5p1p23,
    J(R)=Jθ,p,q,r,C0(R)=2pq4p3exp(pθ412θln(6pq4)).

    Lemma 2.2. ([15]) Let η be the past history variable and (1.2) holds. Then

    ηt(s)2MXητ(s)2MX2tτ((ησ,u(σ)))MXdσ.

    We assume that f1(x) and f2(t,x) satisfy the following hypotheses:

    (C1) The function f1H.

    (C2) f2(t,x) is translation bounded in L2loc(R,H), which means there exists a constant K>0 such that

    suptRt+1t|f2(s)|2ds<K,

    and for any tR, there also holds

    teιsf2(s)2ds<, 0<ινε0, ν=min{ακδ72,1}, (3.1)

    where κ,δ are the same as parameters in (2.1) and (2.2) respectively.

    Construct the infinitesimal generator of right-translation semigroup on MX

    Tη=sη,

    whose domain is

    D(T)={ηMX: sηMX,  η(0)=0}.

    Given initial datum U(τ)=(u(τ),ητ)NV, then (1.1) can be transformed into the following abstract form

    {t(u+αAu)+0μ(s)Aη(s)ds+B(u,u)+βu=PLfε(t,x), (t,x)Ωτ,tη=Tη+u,divu=0, (t,x)Ωτ,u(t,x)=0, (t,x)Ωτ,u(τ,x)=u(τ), xΩ,ητ(s)=s0φ(σ)dσ. (3.2)

    ● Global well-posedness of solution

    Definition 3.1 A function U(t)=(u(t),ηt): [τ,+)NV is called the weak solution to (3.2), if for any fixed T>τ there hold

    (i) U(t)C([τ,T];NV), utL2(τ,T;V).

    (ii) U(τ)=(u(τ),ητ).

    (iii) for any wC1([τ,T];V) with w(T,x)=0, there holds

    Tτu+αAu,wtdt+Tτ0μ(s)((η(s),w))Vdsdt+Tτb(u,u,w)dt+Tτ(βu,w)dt=((u(τ),w(τ)))Vα+Tτ(PLfε,w)dt. (3.3)

    Theorem 3.2. Let U(τ)NV, and the hypotheses (C1)–(C2) hold. Then the global weak solution U(t,x) to system (3.2) uniquely exists on (τ,T), which generates a strongly continuous process

    Sε(t,τ): NVNV,  tτ,0ε<ε0

    and Sε(t,τ)U(τ)=U(t).

    Proof. The global well-posedness of solution can be obtained by the Galerkin approximation method, energy estimates and compact scheme. The detailed proof can be found in [15,18] and is omitted here.

    ● Existence of D-pullback attractors

    Theorem 3.3. Assume U(τ)NV and the hypotheses (C1)–(C2) hold. Then the process Sε(t,τ): NVNV generated by the system (3.2) possesses a minimal family of D-pullback attractors Aε={Aε(t)}tR in NV.

    Proof. See Section 4.2.

    When ε=0, the system (3.2) can be reduced to the following autonomous system

    {t(u+αAu)+0μ(s)Aη(s)ds+B(u,u)+βu=PLf1(x), (t,x)Ωτ,tη=Tη+u,divu=0, (t,x)Ωτ,u(t,x)=0, (t,x)Ωτ,u(τ,x)=u(τ), xΩ,ητ(s)=s0φ(σ)dσ. (3.4)

    Remark 3.1. The existence of global attractor A0 in NV can be achieved for the semigroup S0(tτ) generated by (3.4).

    ● Robustness: upper semi-continuity of D-pullback attractors

    Let be a metric space, and {Aλ}λ is a family of subsets in X. Then it is said that {Aλ} has the property of upper semi-continuity as λλ0 in X if

    limλλ0distX(Aλ,Aλ0)=0.

    The upper semi-continuity of attractors and related conclusions can be referred to [1,19,20,22] for more details.

    In the following way, we intend to establish some results on the convergence between D-pullback attractors Aε to system (3.2) and global attractor A0 to system (3.4) as ε0.

    Theorem 3.4. Let U(τ)NV, Aε is the family of D-pullback attractors of Sε(t,τ) in NV to system (3.2), and A0 is the global attractor of S0(tτ) in NV to system (3.4). Then the robustness of system is obtained by the following upper semi-continuity

    limε0distNV(Aε,A0)=0.

    Proof. See Section 5.

    In this section, we first give the fundamental theory of attractors for dissipative systems, and the related conclusions can be seen in [1,2,3,7].

    ● Some relevant definitions

    Definition 4.1. Assume that P(X) is the family of all nonempty subsets in a metric space X. If D is some nonempty class of families in the form ˆD={D(t):tR}P(X), where D(t)X is nonempty and bounded, then D is said to be a universe in P(X).

    Definition 4.2. The family ˆD0={D0(t):tR}P(X) is D-pullback absorbing for the process S(,) on X if for any tR and any ˆDD, there exists a τ0(t,ˆD)t such that

    S(t,τ)D(τ)D0(t),  ττ0(t,ˆD).

    Definition 4.3. A process S(,) on X is said to be D-pullback asymptotically compact if for any tR, any ˆDD, and any sequences {τn}(,t] and {xn}X satisfying τn and xnD(τn), the sequence {S(t,τn)xn} is relatively compact in X.

    The D-pullback asymptotic compactness can be characterized by the Kuratowski measure of noncompactness κ(B) (BX), relating definition and properties can be referred to [25,26], and the definition of Dκ-pullback contraction will be given as follows.

    Definition 4.4. For any tR and ε>0, a process S(t,τ) on X is said to be Dκ-pullback contracting if there exists a constant TD(t,ε)>0 such that

    κ(S(t,tτ)D(tτ))ε,  τTD(t,ε).

    Definition 4.5. A family A(t)={A(t)}tR is called the D-pullback attractors of process S(t,τ), if for any tR and any {D(t)}D, the following properties hold.

    (i) A(t) is compact in X.

    (ii) S(t,τ)A(τ)=A(t), tτ.

    (iii) limτdistX(S(t,τ)D(τ),A(t))=0.

    In addition, D-pullback attractor A is said to be minimal if whenever ˆC is another D-attracting family of closed sets, then A(t)C(t) for all tR.

    ● Some conclusions

    Theorem 4.6. ([1,27]) Let S(,):R2d×XX be a continuous process, where R2d={(t,τ)R2|tτ}, D is a universe in P(X), and a family ˆD0={D0(t):tR}P(X) is D-pullback absorbing for S(,), which is D-pullback asymptotically compact. Then, the family of D-pullback attractors AD={AD(t):tR} exists and

    AD(t)=s0¯τsS(t,tτ)D(tτ)X, tR.

    Remark 4.1. If ˆD0D, then AD is minimal family of closed subsets attracting pullback to D. It is said to be unique provided that ˆD0D, D0(t) is closed for any tR, and D is inclusion closed.

    Theorem 4.7. ([21]) Assume that ˜D={ˆ˜D(t)} is a family of sets in X, S(,) is continuous, and, for any tR, there exists a constant T(t,D,˜D) such that

    S(t,tτ)D(tτ)˜D(t),  τT(t,D,˜D).

    If S(,) is D-pullback absorbing and ˆDκ-pullback contracting, then the D-pullback attractors AD={AD(t):tR} exist for S(,).

    Lemma 4.8. ([28]) Assume that S(,)=S1(,)+S2(,), ˜D={ˆ˜D(t)} is a family of subsets in X, and for any tR and any τR+ there hold

    (i) For any u(tτ)˜D(tτ),

    S1(t,tτ)u(tτ)XΦ(t,τ)0 (τ+).

    (ii) For any Tτ, 0τTS2(t,tτ)˜D(tτ) is bounded, and S2(t,tτ)˜D(tτ) is relatively compact in X.

    Then S(,) is ˆDκ-pullback contracting in X.

    From Theorems 3.2, we know that the system (3.2) generates a continuous process Sε(t,τ) in NV. To obtain the D-pullback attractors, we need to establish the existence of D-pullback absorbing set and the D-pullback asymptotic compactness of Sε(t,τ).

    ● Existence of D-pullback absorbing set in NV

    Let D denote a family of all {D(t)}tRP(NV) satisfying

    limτeωτsupU(τ)D(τ)J(2|U(τ)|2)=0,

    where ω=ω3/4,1,4,3,fε>0 and J()=J3/4,1,4,3,fε(). Next, we establish the existence of D-pullback absorbing set.

    Lemma 4.9. Let (u(τ),ητ)NV, then the process {Sε(t,τ)} to system (3.2) possesses a D-pullback absorbing set ˆDε0(t)={Dε0(t)}tR in NV, where

    Dε0(t)=ˉBNV(0,ρεNV(t)),

    with radius

    ρεNV(t)=2Λ3/4,1,4,3,fε(2C(|f1|2+εK)+1). (4.1)

    Proof. Multiplying (3.2) by u, we have

    12ddtu2Vα+0μ(s)Aη(s)u(t)ds+β|u|2=(PLfε,u), (4.2)

    that is

    12ddtu2Vα+0μ(s)Aη(s)(tη(s)+sη(s))ds+β|u|2=12ddt(u2Vα+η2MV)+120μ(s)ddsη2ds+β|u|2|(fε,u)|. (4.3)

    Multiplying (3.2) by ut, we have

    ut2Vα+((η,ut))MV+12βddt|u|2+b(u,u,ut)=(PLfε,ut). (4.4)

    Then, the interpolation inequality and Young inequality lead to

    βddt|u|2+2ut2Vα2|((η,ut))MV|+2|(fε,ut)|+2|b(u,u,ut)|2|((η,ut))MV|+2|(fε,ut)|+CuL3uuL62|((η,ut))MV|+2|(fε,ut)|+C|u|1/2u1/2uuL6αut2+Cη2MV+C|u|u3+C|fε|2. (4.5)

    To estimate the term 0μ(s)ddsη2ds in (4.3) and avoid the possible singularity of μ at zero, we refer to [18] and construct the following new function

    ˜μ(s)={μ(˜s),0<s˜s,μ(s),s>˜s

    where ˜s is fixed such that ˜s0μ(s)dsκ/2. Also, if we set

    Φ(t)=4κ0˜μ(s)((η(s),u(t)))ds,

    then differentiating in t leads to

    ddtΦ(t)+u24μ(˜s)κ20μ(s)ddsη2ds+4ακεη2MV+αεut2. (4.6)

    We use the technique in [18] and set

    yε(t)=E(t)+νεΦ(t)+ε2Ψ(t),

    where

    E(t)=12(u2Vα+η2MV), Ψ(t)=2β|u|2.

    For sufficient small ε, it leads to

    E(t)2yε(t)2(E(t)+1),

    where we choose ε0 satisfying

    νε0supt[τ,T]Φ(t)+ε20supt[τ,T]Ψ(t)=1,

    and 0ε<ε0. Then, there holds

    ddtyε(t)+Cεyε(t)Cε4yε(t)3+C|fε|2, (4.7)

    and

    yε(t)yε(τ)eε(tτ)+Cε4tτeε(ts)1yε(s)3ds+CsuptRt+1t|fε|2ε1dsyε(τ)eε(tτ)+Cε4tτeε(ts)1yε(s)3ds+C(|f1|2+εK)ε1. (4.8)

    Then by Lemma 2.1, there exist

    ω=ω3/4,1,4,3,fε>0, Λ=Λ3/4,1,4,3,fε>0,

    and an increasing function

    J()=J3/4,1,4,3,fε(): R+R+

    such that

    E(t)J(2E(τ))eω(tτ)+Λ(2C(|f1|2+εK)+1),

    which implies the conclusion holds.

    Remark 4.2. For the semigroup S0(tτ), it has the global absorbing set D00 in NV, where

    D00={UNV; UNVρ0NV=2Λ3/4,1,4,3,f1(2C|f1|2+1)} (4.9)

    and

    lim supε0ρεNV(t)=ρ0NV. (4.10)

    Dκ-pullback contraction of Sε(t,τ) in NV

    To verify the pullback contraction of Sε(t,τ), we decompose Sε(t,τ) as follows

    Sε(t,τ)U(τ)=Sε1(tτ)U1(τ)+Sε2(t,τ)U2(τ)=:U1(t)+U2(t),

    which solve the following two problems respectively

    {t(u1+αAu1)+0μ(s)Aη1(s)ds+B(u,u1)=0, (t,x)Ωτ,tη1=Tη1+u1,divu1=0, (t,x)Ωτ,u1(t,x)=0, (t,x)Ωτ,u1(τ,x)=u(τ), xΩ,ητ1(s)=s0φ(σ)dσ, (4.11)

    and

    {t(u2+αAu2)+0μ(s)Aη2(s)ds+B(u,u2)+βu2=PLfεβu1, (t,x)Ωτ,tη2=Tη2+u2,divu2=0, (t,x)Ωτ,u2(t,x)=0, (t,x)Ωτ,u2(τ,x)=0, xΩ,ητ2(s)=0. (4.12)

    Lemma 4.10. Let U(τ)Dε0(τ), then the solution Sε1(tτ)U(τ) to the system (4.11) satisfies

    Sε1(tτ)U(τ)NVJ(2E(τ))eω(tτ)0 (τ).

    Proof. Multiplying (4.11) by u1 and tu1 respectively, and repeating the reasonings as shown as in Lemma 4.9, in which β=0 and fε=0, we can derive the conclusion finally. The parameter ω is dependent on ε and the increasing function J() is different from the one in Lemma 4.9. Despite all this, these parameters can be unified in same representation, and the concrete details are omitted here.

    Lemma 4.11. Let U(τ)Dε0(τ), then for any tR, there exists Cε(t)>0 such that the solution Sε2(t,τ)U(τ) to the system (4.12) satisfies

    Sε2(t,τ)U(τ)NWCε(t).

    Proof. Multiplying (4.12) by Au2, we have

    12ddt(u22+α|Au2|2)+0μ(s)Aη2(s)Au2(t)ds+βu22+b(u,u2,Au2)=(PLfεβu1,Au2), (4.13)

    from the existence of pullback absorbing set, Lemma 4.10, the interpolation inequality and Young inequality, we have

    12ddt(u22+α|Au2|2+η22MW)+120μ(s)dds|Aη2|2ds+βu22|(fε,Au2)|+|(βu1,Au2)|+uL6u2L3|Au2|νε4|Au2|2+Cuu21/2|Au2|1/2|Au2|+C|fε|2νε2|Au2|2+C|fε|2+C. (4.14)

    Multiplying (4.12) by Atu2, we have

    tu22+α|Atu2|2+((η2,tu2))MW+12βddtu22+b(u,u2,tu2)=(PLfεβu1,Atu2). (4.15)

    By the existence of pullback absorbing set, Lemma 4.10 and Young inequality, one has

    βddtu22+2tu22+2α|Atu2|22|((η2,tu2))MW|+2|(PLfεβu1,Atu2)|+2|b(u,u2,tu2)|2|((η2,tu2))MW|+2|(PLfεβu1,Atu2)|+C|Au2||Atu2|α|Atu2|2+Cη22MW+C|Au2|2+C|fε|2. (4.16)

    To estimate the term 0μ(s)dds|Aη2|2ds in (4.14), we set

    Φ2(t)=6κ0˜μ(s)(Aη2(s),Au2(t))ds,

    and differentiating in t leads to

    ddtΦ2(t)+6κ0˜μ(s)(Au2(t),Au2(t))ds6κ0˜μ(s)(Asη2,Au2(t))ds6κ0˜μ(s)(Aη2(s),Atu2(t))ds, (4.17)

    where

    6κ0˜μ(s)(Au2(t),Au2(t))ds6κ˜s˜μ(s)ds|Au2(t)|2, (4.18)

    and

    6κ0˜μ(s)(Asη2,Au2(t))ds=6κ˜sμ(s)(Aη2,Au2(t))ds6κ˜sμ(s)|Aη2||Au2(t)|ds6κ(˜sμ(s)|Aη2|2ds)1/2(˜sμ(s)|Au2(t)|2ds)1/26κ(˜sμ(s)dds|Aη2|2ds)1/2(2μ(˜s))1/2|Au2(t)||Au2(t)|2+18μ(˜s)κ2˜sμ(s)dds|Aη2|2ds, (4.19)

    and

    6κ0˜μ(s)(Aη2(s),Atu2(t))ds6κ0μ(s)|Aη2(s)||Atu2(t)|dsαε|Atu2(t)|2+9ακ2εη2MW. (4.20)

    Also, from the fact that μ(s)+δμ(s)0, we have

    0μ(s)dds|Aη2|2ds0δμ(s)|Aη2|2ds=δη22MW. (4.21)

    Thus

    ddtΦ2(t)+2|Au2|218μ(˜s)κ20μ(s)dds|Aη2|2ds+9ακεη22MW+αε|Atu2|2. (4.22)

    We use the technique in [18] and set

    zε(t)=E2(t)+νεΦ2(t)+ε2Ψ2(t),

    where

    E2(t)=12(u22+α|Au2|2+η22MW), Ψ2(t)=βu2.

    For sufficient small enough ε, it leads to

    E2(t)2zε(t)2(E2(t)+1),

    and there holds

    ddtzε(t)+νεzε(t)C+C|fε|2, (4.23)

    it follows from the Gronwall lemma that

    E2(t)Ceνε(tτ)E2(τ)+Cεtτeνε(st)|f2(s,x)|2ds+C|f1|2+CCεeνεttτeνε0s|f2(s,x)|2ds+C|f1|2+C, (4.24)

    which means the conclusion holds.

    Above all, Lemmas 4.10, 4.11 and 4.8 lead to

    Lemma 4.12. Let U(τ)NV, then the process Sε(t,τ): NVNV generated by the system (3.2) is Dκ-pullback contracting in NV.

    Consequently, from Theorem 4.7, we can finish the proof of Theorem 3.3.

    By the definition of upper semi-continuity, the following lemmas can be used to obtain the robustness of pullback attractors for evolutionary systems.

    Lemma 5.1. ([20]) Let ε(0,ε0], {Sε(t,τ)} is the process of evolutionary system with non-autonomous term (depending on ε), which is obtained by perturbing the semigroup S0(τ) of system without ε, and, for any tR, there also hold that

    (i) Sε(t,τ) has the pullback attractors Aε(t), and A0 is the global attractor for S0(τ).

    (ii) For any τR+ and any uX, there holds uniformly that

    limε0dX(Sε(t,tτ)u,S0(τ)u)=0.

    (iii) There exists a compact subset GX such that

    limε0distX(Aε(t),G)=0.

    Then, for any tR, there holds

    limε0distX(Aε,A0)=0.

    Lemma 5.2. ([21]) For any tR, τR+, and ε(0,ε0], ˆDε0(t)={Dε0(t):tR} is the pullback absorbing set for Sε(t,τ), and ˆCε0(t)={Cε0(t):tR} is a family of compact subsets in X. Assume that Sε(,)=Sε1(,)+Sε2(,), and there hold

    (i) For any utτDε0(tτ),

    Sε1(t,tτ)utτXΦ(t,τ)0 (τ).

    (ii) For any Tτ, 0τTSε2(t,tτ)Dε0(tτ) is bounded, and there exists a constant TDε0(t), independent of ε, such that

    Sε2(t,tτ)Dε0(tτ)Cε0(t),  τ>TDε0(t).

    (iii) There is a compact subset GX such that

    limε0distX(Cε0(t),G)=0.

    Then, the process Sε(t,τ) has the pullback attractors Aε(t), and

    limε0distX(Aε,G)=0.

    We give the following procedure to verify Theorem 3.4.

    Lemma 5.3. Let (uε,ηε)=Sε(t,τ)U(τ) be the solution to system (3.2), and (u,η)=S0(tτ)U(τ) is the solution to system (3.4), then, for any bounded subset BNV, there holds

    limε0supU(τ)BdNV(Sε(t,τ)U(τ),S0(tτ)U(τ))=0.

    Proof. We know

    {t(uε+αAuε)+0μ(s)Aηε(s)ds+B(uε,uε)+βuε=PLfε(t,x), (t,x)Ωτ,tηε=Tηε+uε, (5.1)

    and

    {t(u+αAu)+0μ(s)Aη(s)ds+B(u,u)+βu=PLf1(x), (t,x)Ωτ,tη=Tη+u. (5.2)

    Let wε=uεu and ξε=ηεη, we can derive

    t(wε+αAwε)+0μ(s)Aξε(s)ds+B(uε,wε)+B(wε,u)+βwε=εPLf2(t,x), (5.3)

    and multiplying it by wε leads to

    12ddtwε2α+0μ(s)(Aξε(s),wε(t))ds+b(wε,u,wε)+β|wε|2=ε(PLf2(t,x),wε), (5.4)

    it follows that

    12ddtwε2α+0μ(s)((ξε(s),wε))ds|b(wε,u,wε)|+ε|(f2(t,x),wε)|. (5.5)

    Integrating (5.5) over [τ,t], from Lemma 2.2 we derive that

    wε(t)2α+ξε(ts)2MVwε(τ)2α+ξε(τs)2MV+2tτ|b(wε,u,wε)|ds+2εtτ|(f2(t,x),wε)|ds(wε,ξε)|τ2NV+Ctτuwε2ds+2εtτ|(f2(t,x),wε)|ds(wε,ξε)|τ2NV+ε2tτ|f2|2ds+Ctτwε2ds, (5.6)

    that is

    (wε,ξε)|t2NV(wε,ξε)|τ2NV+ε2tτ|f2|2ds+Ctτ(wε,ξε)|s2NVds, (5.7)

    and the Gronwall inequality leads to

    (wε,ξε)|t2NVC((wε,ξε)|τ2NV+ε2tτ|f2|2ds)0 (ε0), (5.8)

    which means that the conclusion is finished.

    Proof of Theorem 3.4. From (4.24) and the fact that WV is compact, we know that there exists a compact subset GNV such that

    limε0distX(Cε0(t),G)=0. (5.9)

    Combining Lemma 4.10, Lemma 5.2 and (5.9), we have

    limε0distX(Aε,G)=0.

    In addition, the confirmation of condition (ii) in Lemma 5.1 is finished from Lemma 5.3, and we have

    limε0distX(Aε,A0)=0.

    Rong Yang was partially supported by the Science and Technology Project of Beijing Municipal Education Commission (No. KM202210005011).

    The authors declare there is no conflict of interest.



    [1] B. Ahmad, P. A. Floor, I. Farup, Ø. Hovde, 3D reconstruction of gastrointestinal regions using single-view methods, IEEE Access, 11 (2023), 61103–61117. https://doi.org/10.1109/ACCESS.2023.3286937 doi: 10.1109/ACCESS.2023.3286937
    [2] Z. Cui, J. Feng, J. Zhou, Monocular 3D fingerprint reconstruction and unwarping, IEEE Trans. Pattern Anal. Mach. Intell., 45 (2023), 8679–8695. https://doi.org/10.1109/TPAMI.2022.3233898 doi: 10.1109/TPAMI.2022.3233898
    [3] H. Choi, M. Lee, J. Kang, D. Lee, Online 3D edge reconstruction of wiry structures from monocular image sequences, IEEE Rob. Autom. Lett., 8 (2023), 7479–7486. https://doi.org/10.1109/LRA.2023.3320022 doi: 10.1109/LRA.2023.3320022
    [4] Y. Ding, Z. Chen, Y. Ji, J. Yu, J. Ye, Light field-based underwater 3D reconstruction via angular re-sampling, IEEE Trans. Comput. Imaging, 9 (2023), 881–893. https://doi.org/10.1109/TCI.2023.3319983 doi: 10.1109/TCI.2023.3319983
    [5] M. Pistellato, F. Bergamasco, A. Torsello, F. Barbariol, J. Yoo, J. Y. Jeong, et al., A physics-driven CNN model for real-time sea waves 3D reconstruction, Remote Sens., 13 (2021), 3780. https://doi.org/10.3390/rs13183780 doi: 10.3390/rs13183780
    [6] Y. Liang, X. Fan, Y. Yang, D. Li, T. Cui, Oblique view selection for efficient and accurate building reconstruction in rural areas using large-scale UAV images, Drones, 6 (2022), 175. https://doi.org/10.3390/drones6070175 doi: 10.3390/drones6070175
    [7] Z. Hu, Y. Hou, P. Tao, J. Shan, IMGTR: Image-triangle based multi-view 3D reconstruction for urban scenes, ISPRS J. Photogramm. Remote Sens., 181 (2021), 191–204. https://doi.org/10.1016/j.isprsjprs.2021.09.009 doi: 10.1016/j.isprsjprs.2021.09.009
    [8] J. Pan, L. Li, H. Yamaguchi, K. Hasegawa, F. I. Thufail, Brahmantara, et al., 3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning, ISPRS J. Photogramm. Remote Sens., 183 (2022), 439–450. https://doi.org/10.1016/j.isprsjprs.2021.11.007 doi: 10.1016/j.isprsjprs.2021.11.007
    [9] J. Zhang, L. Zhao, K. Yu, G. Min, A. Y. Al-Dubai, A. Y. Zomaya, A novel federated learning scheme for generative adversarial networks, IEEE Trans. Mob. Comput., 2023 (2023), 1–17. https://doi.org/10.1109/TMC.2023.3278668 doi: 10.1109/TMC.2023.3278668
    [10] Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, et al., Learning semantic segmentation of large-scale point clouds with random sampling, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2021), 8338–8354. https://doi.org/10.1109/TPAMI.2021.3083288 doi: 10.1109/TPAMI.2021.3083288
    [11] Z. Guo, K. Yu, Z. Lv, K. K. R. Choo, P. Shi, J. J. P. C. Rodrigues, Deep federated learning enhanced secure POI microservices for cyber-physical systems, IEEE Wireless Commun., 29 (2022), 22–29. http://doi.org/10.1109/MWC.002.2100272 doi: 10.1109/MWC.002.2100272
    [12] L. Bai, Y. Li, M. Cen, F. Hu, 3D instance segmentation and object detection framework based on the fusion of Lidar remote sensing and optical image sensing, Remote Sens., 13 (2021), 3288. https://doi.org/10.3390/rs13163288 doi: 10.3390/rs13163288
    [13] J. Yang, L. Jia, Z. Guo, Y. Shen, X. Li, Z. Mou, et al., Prediction and control of water quality in Recirculating Aquaculture System based on hybrid neural network, Eng. Appl. Artif. Intell., 121 (2023), 106002. https://doi.org/10.1016/j.engappai.2023.106002 doi: 10.1016/j.engappai.2023.106002
    [14] J. Huang, F. Yang, C. Chakraborty, Z. Guo, H. Zhang, L. Zhen, et al., Opportunistic capacity based resource allocation for 6G wireless systems with network slicing, Future Gener. Comput. Syst., 140 (2023), 390–401. https://doi.org/10.1016/j.future.2022.10.032 doi: 10.1016/j.future.2022.10.032
    [15] B. Gecer, S. Ploumpis, I. Kotsia, S. Zafeiriou, Fast-GANFIT: Generative adversarial network for high fidelity 3D face reconstruction, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2021), 4879–4893. https://doi.org/10.1109/TPAMI.2021.3084524 doi: 10.1109/TPAMI.2021.3084524
    [16] G. Hou, W. Zhang, B. Wu, R. He, 3D reconstruction and positioning of surface features based on a monocular camera and geometric constraints, Appl. Opt., 61 (2022), C27–C36. https://doi.org/10.1364/AO.436234 doi: 10.1364/AO.436234
    [17] X. Zhu, F. Ma, F. Ding, Z. Guo, J. Yang, K. Yu, A low-latency edge computation offloading scheme for trust evaluation in finance-level artificial intelligence of things, IEEE Internet Things J., 2023. https://doi.org/10.1109/JIOT.2023.3297834 doi: 10.1109/JIOT.2023.3297834
    [18] Z. Guo, Q. Zhang, F. Ding, X. Zhu, K. Yu, A novel fake news detection model for context of mixed languages through multiscale transformer, IEEE Trans. Comput. Social Syst., 2023 (2023), 1–11. https://doi.org/10.1109/TCSS.2023.3298480 doi: 10.1109/TCSS.2023.3298480
    [19] J. Yang, Z. Guo, J. Luo, Y. Shen, K. Yu, Cloud-edge-end collaborative caching based on graph learning for cyber-physical virtual reality, IEEE Syst. J., 2023 (2023), 1–12. https://doi/org/10.1109/JSYST.2023.3262255 doi: 10.1109/JSYST.2023.3262255
    [20] Z. Shen, F. Ding, Y. Yao, A. Bhardwaj, Z. Guo, K. Yu, A privacy-preserving social computing framework for health management using federated learning, IEEE Trans. Comput. Social Syst., 10 (2023), 1666–1678. https://doi.org/10.1109/TCSS.2022.3222682 doi: 10.1109/TCSS.2022.3222682
    [21] Z. Zheng, T. Yu, Y. Liu, Q. Dai, Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2022), 3170–3184. https://doi.org/10.1109/TPAMI.2021.3050505 doi: 10.1109/TPAMI.2021.3050505
    [22] D. Meng, Y. Xiao, Z. Guo, A. Jolfaei, L. Qin, X. Lu, et al., A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things, Comput. Commun., 179 (2021), 231–241. https://doi.org/10.1016/j.comcom.2021.08.014 doi: 10.1016/j.comcom.2021.08.014
    [23] Q. Zhang, Z. Guo, Y. Zhu, P. Vijayakumar, A. Castiglione, B. B. Gupta, A deep learning-based fast fake news detection model for cyber-physical social services, Pattern Recognit. Lett., 168 (2023), 31–38. https://doi.org/10.1016/j.patrec.2023.02.026 doi: 10.1016/j.patrec.2023.02.026
    [24] J. Chen, W. Wang, K. Yu, X. Hu, M. Cai, M. Guizani, Node connection strength matrix-based graph convolution network for traffic flow prediction, IEEE Trans. Veh. Technol., 72 (2023), 12063–12074. https://doi.org/10.1109/TVT.2023.3265300 doi: 10.1109/TVT.2023.3265300
    [25] X. Yuan, H. Tian, Z. Zhang, Z. Zhao, L. Liu, A. K. Sangaiah, et al., A MEC offloading strategy based on improved DQN and simulated annealing for internet of behavior, ACM Trans. Sens. Netw., 19 (2023), 1–20. https://doi.org/10.1145/3532093 doi: 10.1145/3532093
    [26] S. Han, L. Huo, Y. Wang, J. Zhou, H. Li, Rapid reconstruction of 3D structural model based on interactive graph cuts, Buildings, 12 (2022), 22. https://doi.org/10.3390/buildings12010022 doi: 10.3390/buildings12010022
    [27] L. Yang, F. Zhang, F. Yang, P. Qian, Q. Wang, Y. Wu, et al., Generating topologically consistent BIM models of utility tunnels from point clouds, Sensors, 23 (2023), 6503. https://doi.org/10.3390/s23146503 doi: 10.3390/s23146503
    [28] Y. Yin, G. Liu, S. Li, Z. Zheng, Y. Si, Y. Wang, A method for predicting canopy light distribution in cherry trees based on fused point cloud data, Remote Sens., 15 (2023), 2516. https://doi.org/10.3390/rs15102516 doi: 10.3390/rs15102516
    [29] Y. Peng, S. Lin, H. Wu, G. Cao, Point cloud registration based on fast point feature histogram descriptors for 3D reconstruction of trees, Remote Sens., 15 (2023), 3775. https://doi.org/10.3390/rs15153775 doi: 10.3390/rs15153775
    [30] A. Vong, J. P. Matos-Carvalho, P. Toffanin, D. Pedro, F. Azevedo, F. Moutinho, et al., How to build a 2D and 3D aerial multispectral map? –– all steps deeply explained, Remote Sens., 13 (2021), 3227. https://doi.org/10.3390/rs13163227 doi: 10.3390/rs13163227
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